Defect classification method and apparatus, and defect inspection apparatus

ABSTRACT

A defect classification method to classify defects by using a classifier having a binary tree structure based on features of defects extracted from detected signals acquired from a defect inspection apparatus includes a classifier construction process for constructing the classifier by setting a branch condition including defect classes respectively belonging to groups located on both sides of the branch point, a feature to be used for branching, and a discriminant reference, for each branch point in the structure based on instruction of defect classes and feature data respectively associated therewith beforehand. The process includes a priority order specification process for previously specifying target classification performance of purity and accuracy for each defect class, whole and in worst case, with priority order, and an evaluation process for evaluating whether the specified target classification performance under the branching condition is satisfied and displaying a result of evaluation, every item.

INCORPORATION BY REFERENCE

This application is a divisional application of Ser. No. 11/779,905,filed Jul. 19, 2007, which claims priority from Japanese applicationJP2006-262083 filed on Sep. 27, 2006, the content of which is herebyincorporated by reference into this application.

BACKGROUND OF THE INVENTION

The present invention relates to a defect classify and inspectionmethod, and apparatus, to classify defects such as minute patterndefects and dust particles on the basis of an image of an inspectionsubject obtained from a thin film device such as a semiconductor wafer,a TFT or a photomask by using lamp light, laser light or an electronbeam. In particular, the present invention relates to a defectinspection method, and apparatus, suitable for inspecting defects in asemiconductor wafer.

Thin film devices such as semiconductor wafers, liquid crystal displaysand hard disk magnetic heads are manufactured through a large number ofworking processes. In the manufacture of such thin film devices, visualinspection is executed every some series of processes with the object ofyield improvement and stabilization. In the visual inspection, defectssuch as pattern defects or dust particles are detected on the basis of areference image and an inspection image obtained respectively fromcorresponding regions of two patterns formed originally so as to havethe same shape by using lamp light, laser light or an electron beam. Inother words, the reference image is aligned with the inspection image tocalculate a difference, and the difference is compared with a separatelydetermined threshold to detect a part having a large difference as adefect or a dust particle. At the same time, features, such as theluminance and size, of a defect are calculated from an image of thedefect part and the defect is sorted on the basis of the features, insome cases.

For example, an inspection apparatus that sorts a dust particle which isa convex defect and a scratch which is a concave defect according to adifference in scattered light intensity caused by vertical illuminationand oblique illumination is disclosed in JP-A-2002-257533 (PatentDocument 1). When determining a defect sort condition of the inspectionapparatus having such a defect sort function, it is necessary toinstruct a class to be sorted into by using a review and derive arelation between features and the class. In the above-described example,the class to be sorted into is either a dust particle or a scratch. Thescattered light intensity under the vertical illumination and thescattered light intensity under the oblique illumination are used as thefeatures. A discriminant line is set manually on the basis of atwo-dimensional scatter diagram.

In addition, there are instruction type and rule base type in sorttechniques. In the instruction type, a sorter is automaticallyconstructed by instruction of feature data associated with a correctanswer class. In a method used in sort of the instruction type, a defectis sorted into a class of already taught defects having a shortestdistance in the feature space. In another method used in sort of theinstruction type, feature distribution of each defect class is presumedon the basis of instruction data and a defect is sorted into a class inwhich the occurrence probability of the features of a defect to besorted is the highest. The rule base type is a method of sorting defectsaccording to a rule described in the “if-then-else” form. In many cases,the rule is represented by a threshold for a feature. The classifymethod described in Patent Document 1 is also a kind of the rule basetype.

A method for generating a defect classifier described in JP-A-2004-47939(Patent Document 2) includes an inspection information acquisition stepof inspecting a defective sample group on an arbitrary sample by usingat least an arbitrary defect inspection apparatus and acquiring sampleinspection information, and a decision tree setting step. The decisiontree setting step includes a display step of displaying a state ofdefect attribute distribution of a defective sample group on thearbitrary sample, on a screen on the basis of the sample inspectioninformation acquired at the inspection information acquisition step. Thedecision tree setting step further includes a classify rule setting stepof setting an individual classify rule for each of branch elements in adecision tree, which hierarchically develops sort class elements of thedefective sample group via branch elements, on the basis of the state ofthe defect attribute distribution displayed on the screen.

SUMMARY OF THE INVENTION

The defect sort of the rule base type has an advantage that classifycondition setting with a user's intention reflected therein on the basisof the theory and experience is possible and the user can easilyunderstand the classify condition setting. However, the defect classifyof the rule base type has a problem that it is difficult to set all ofthe classify condition manually if feature kinds or defect class kindshave increased.

On the other hand, in the defect classify of the instruction type, theclassify condition is automatically set if data is input. However, thedefect classify of the instruction type has a problem that the user'sintention cannot be reflected and the classify condition cannot beinterpreted, either. The user's intention is, for example, to conductadjustment so as to make the purity, accuracy or both of them equal toat least a target value(s), to intentionally avoid use of a certainfeature, or to determine features to be used, on the basis of knowledge.

In order to solve the above-described problems, the present inventionprovides a defect classification method, and apparatus, and a defectinspection apparatus that makes it possible to classify defects by usinga binary tree structure or an instruction type classifier having aclassify condition setting function that can reflect the user'sintention.

In accordance with the present invention, in a defect classificationmethod, and apparatus, to classify defects by using a classifier havinga binary tree structure on the basis of features of the defectsextracted on the basis of detected signals acquired from a defectinspection apparatus includes a classifier construction process forconstructing the classifier having the binary tree structure by settinga branch condition for each branch point in the binary tree structure onthe basis of instruction of defect classes and feature data respectivelyassociated therewith beforehand, the branch condition including defectclasses respectively belonging to groups located on both sides of thebranch point, a feature to be used for branch, and a discriminantreference. In the classifier having the binary tree structure, defectsare finally classified into desired classes by repetitively bisectingdefect data. The branch condition setting at each branch point includesa step of determining details of classes to be bisected, a step ofdetermining a feature to be used for branch, and a step of determining adiscriminant reference (classify reference) such as a threshold orprobability distribution.

In accordance with the present invention, the classifier constructionprocess can use both automatic setting and manual setting as the branchcondition setting. In other words, as for branch condition setting,three steps are executed automatically, or details are determinedmanually and remaining steps are executed automatically, or details andfeatures are set manually and remaining steps are executedautomatically, or all three steps are set manually. In theconfiguration, any of them can be selected. In the configuration, it isalso possible to conduct conduction setting automatically at a selectedbranch point and the subsequent structure.

In accordance with the present invention, the classifier constructionprocess includes a display process for displaying information thatrepresents feature distribution by defect classes and information thatrepresents an evaluation result of classify performance under the setbranch condition, every branch point in the binary tree structure. Inother words, manual setting is supported by a configuration thatdisplays a histogram by defect classes and correct answer ratios bydefect classes with respect to a specified branch point.

In accordance with the present invention, the classifier constructionprocess includes a priority order specification process for previouslyspecifying target sort performance of purity and accuracy for each ofdefect classes, whole and in worst case, with priority order; and anevaluation process for evaluating whether the specified target sortperformance under the set branch condition is satisfied every item anddisplaying a result of evaluation every item. In other words, a functionof conducting specification with priority order as the user's intentionis provided. It is evaluated and displayed with respect to a specifiedbranch point whether each of items of the user's intention is satisfied.

In accordance with the present invention, a defect classificationmethod, and apparatus, to classify defects by using a classifier ofinstruction type (a classify algorithm of instruction type) on the basisof features of the defects extracted on the basis of detected signalsacquired from a defect inspection apparatus includes a priority orderspecification process for previously specifying target sort performanceof purity and accuracy for each of defect classes, whole and in worstcase, with priority order; and a sorter construction process forconstructing the classifier of instruction type by setting a classifycondition by means of learning using a learning algorithm and a learningparameter specified beforehand, on the basis of instruction of defectclasses and feature data respectively associated therewith beforehand.In the classifier construction process, classification performance underthe set classify condition is evaluated and whether the specified targetclassification performance is satisfied is displayed every item.

In accordance with the present invention, a defect classificationmethod, and apparatus, to classify defects by using a classifier ofinstruction type (a classify algorithm of instruction type) on the basisof features of the defects extracted on the basis of detected signalsacquired from a defect inspection apparatus includes a priority orderspecification process for previously specifying target sort performanceof purity and accuracy for each of defect classes, whole and in worstcase, with priority order; and a sorter construction process forconstructing the classifier of instruction type by evaluating whetherthe specified target sort performance is satisfied every item whilecomprehensively changing a learning algorithm and a learning parameterof the classifier of instruction type on the basis of instruction ofdefect classes and feature data respectively associated therewithbeforehand, searching for a learning algorithm and a learning parameterthat are favorable in a result of evaluation conducted every item, andsetting a classify condition on the basis of learning using the learningalgorithm and learning parameter obtained by the searching.

In accordance with the present invention, the features to classifydefects by using the classifier are obtained by unifying features ofdefects extracted on the basis of detected signals acquired byrespective inspections under a plurality of different conditions in thedefect inspection apparatus, after conducting matching of defectcoordinates.

In accordance with the present invention, when sorting defects by usinga classifier on the basis of the features of the defects, defect classesare determined by individually using features of the defect extracted onthe basis of detected signals acquired by respective inspections under aplurality of different conditions in the defect inspection apparatus,individually making defect classify decisions according to a pluralityof defect classify conditions preset for the classifier, and unifyingresults of the individual defect classify decisions by means of weightedvoting of reliability.

These and other objects, features and advantages of the invention willbe apparent from the following more particular description of preferredembodiments of the invention, as illustrated in the accompanyingdrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic configuration diagram showing an embodiment of adefect inspection apparatus (visual inspection apparatus) according tothe present invention;

FIG. 2 is a plan view of a semiconductor wafer to be inspected,according to the present invention;

FIG. 3 is a diagram for explaining an embodiment of a structure of aclassifier having a binary tree structure according to the presentinvention;

FIG. 4 is a diagram for explaining an embodiment of a flow of data of aclassify condition setting unit included in classifier constructionmeans according to the present invention;

FIGS. 5A, 5B and 5C are a diagram showing a first embodiment of aclassify condition setting GUI included in classifier construction meansaccording to the present invention;

FIG. 6 is a diagram showing an embodiment of an automatic setting screenof a classify condition according to the present invention;

FIG. 7 is a diagram showing an embodiment of a flow of branch conditionsetting in the branch condition setting unit included in classifierconstruction means according to the present invention;

FIGS. 8A, 8B and 8C are a diagram showing a display state of a GUI givenwhen classify condition setting is started, according to the presentinvention;

FIG. 9 is a diagram showing an embodiment of a GUI for manually settinga discriminant reference, according to the present invention;

FIG. 10 is a diagram showing an embodiment of display of a treestructure on the way of setting a classify condition for constructing(generating) a sorter having a binary tree structure according to thepresent invention;

FIG. 11 is a diagram showing an embodiment of a flow of processing forautomatically setting a branch condition following a branch pointselected to construct (generate) a sorter having a binary tree structureaccording to the present invention;

FIGS. 12A-12F are a diagram for explaining an algorithm thatautomatically sets a branch condition for constructing (generating) asorter having a binary tree structure according to the presentinvention;

FIGS. 13A to 13D are a diagram showing an embodiment of a GUI forinstructing a defect class in a classify condition setting unit includedin classifier construction means according to the present invention;

FIG. 14 is a diagram showing another embodiment of a detection unit in adefect inspection apparatus (visual inspection apparatus) according tothe present invention;

FIG. 15 is a schematic configuration diagram showing another embodimentof a defect inspection apparatus (visual inspection apparatus) accordingto the present invention;

FIG. 16 is a diagram showing a second embodiment of a GUI for specifyinguser's intention with priority orders in a sort condition setting unitincluded in classifier construction means according to the presentinvention;

FIG. 17 is a diagram showing an embodiment of a classify conditionsetting flow for constructing (generating) a classifier (classifyalgorithm) of instruction type other than the binary tree structureaccording to the present invention;

FIG. 18 is a diagram showing a second embodiment of a GUI that displaysa satisfaction evaluation result of user's intention in classifycondition setting for constructing (generating) a classifier (classifyalgorithm) of instruction type according to the present invention;

FIG. 19 is a diagram showing an embodiment of a classify conditionsetting flow for constructing (generating) a classifier having a binarytree structure according to the present invention;

FIGS. 20A, 20B and 20C are a diagram showing a second embodiment of aGUI that displays a satisfaction evaluation result of user's intentionin classify condition setting for constructing (generating) a classifierhaving a binary tree structure according to the present invention;

FIGS. 21A-21D are a diagram for explaining an embodiment of amodification method of a binary tree branch condition according to thepresent invention; and

FIG. 22 is a diagram showing an embodiment of a flow for evaluatingsatisfaction of a user's intention while conducting algorithm selectionand parameter setting automatically and comprehensively and searchingfor an algorithm and a parameter that make the satisfaction the highest,in classify condition setting for constructing (generating) a classifier(sort algorithm) of instruction type according to the present invention.

DESCRIPTION OF THE EMBODIMENTS

Embodiments of a defect classification method, and apparatus, and adefect inspection method, and apparatus, having a sort condition settingfunction capable of reflecting user's intention, according to thepresent invention will be described with reference to the drawings.

First Embodiment

The first embodiment of a defect classification method, and apparatus,and a defect inspection method, and apparatus, in which defects areclassified by using a classifier constructed (generated) with a classifycondition setting function according to the present invention will bedescribed in detail with reference to FIGS. 1 to 15.

As the first embodiment, the case of an optical defect inspectionapparatus (optical visual inspection apparatus) for semiconductor waferswill be described. FIG. 1 shows an embodiment of a configuration of theoptical defect inspection apparatus. The first embodiment is notrestricted to optical defect inspection apparatuses, but is applicableto electron beam type defect inspection apparatuses or the like, aswell. Reference numeral 11 denotes an inspection subject such as asemiconductor wafer. Reference numeral 12 denotes a stage for mountingand moving the inspection subject 11. Reference numeral 13 denotes adetection unit. The detection unit 13 includes a light source 101 forirradiating the inspection subject 11, an illumination optical system102 for condensing light emitted from the light source 101, an objectlens 103 for forming an optical image obtained by illuminating theinspection subject 11 with illumination light condensed by theillumination optical system 102 resulting in reflection, and an imagesensor 104 for converting the formed optical image to an image signalaccording to brightness. Reference numeral 14 denotes an imageprocessing unit, which detects defect candidates on a wafer serving as asample by using an image detected by the detection unit 13. The lightsource 101 is, for example, a lamp light source or a laser light source.The image sensor 104 is, for example, a CCD linear sensor, a TDI sensoror a photomultiplier.

The image processing unit 14 includes an AD conversion unit 105 forconverting an input signal supplied from the image sensor 104 in thedetection unit 13 to a digital signal, a pre-processing unit 106 forconducting image correction such as shading correction and dark levelcorrection on the digital signal obtained by the AD conversion, a defectdecision unit 107 for comparing a reference image detected from acorresponding position in an adjacent die with a detected image andoutputting a portion where a difference value is greater than aseparately set threshold as a defect, an image cropping unit 108 forextracting the detected image and the reference image with apredetermined size around a position of the detected defect, a featureextraction unit 109 for extracting (calculating) features of the defectfrom the cropped image, and a defect sort unit 110 for sorting thedefects by using a classifier having a binary tree structure or aclassifier of instruction type on the basis of the extracted(calculated) features of the defect. The extracted features of thedefect may be obtained by conducting matching of defect coordinates andunifying features of the defect extracted on the basis of detectedsignals which are acquired by respective inspections under a pluralityof different conditions (including an optical condition and an imageprocessing condition) in the defect inspection apparatus. When classifydefects by using a classifier having a binary tree structure or aclassifier of instruction type on the basis of features of the defect,it is also possible to determine defect classes by individually usingfeatures of the defect extracted on the basis of detected signalsacquired by respective inspections under a plurality of differentconditions (including an optical condition and an image processingcondition) in the defect inspection apparatus, individually makingdefect sort decisions according to a plurality of defect classifyconditions preset for the classifier, and unifying results of theindividual defect class decisions by means of weighted voting ofreliability.

Reference numeral 15 denotes a general control unit. The general controlunit 15 includes a storage 112 for storing coordinates, features and animage of each of detected defects, a user interface unit 113 foraccepting a change of an inspection parameter from the user anddisplaying detected defect information, and a CPU for exercising variouscontrols. Reference numeral 114 denotes a mechanical controller whichdrives the stage 12 on the basis of a control command given by thegeneral control unit. Although not illustrated, the image processingunit 14 and the detection unit 13 are also driven by a command given bythe general control unit 15. Reference numeral 111 denotes a classifycondition setting unit, which sets a sort condition for determining adefect class on the basis of the features of the defect.

Reference numeral 16 denotes a review apparatus, which is not includedin the inspection apparatus, but which can give and receive data.

The GUI using the classify condition setting unit 111 and the userinterface unit 113 inclusive of the review apparatus is thus included insorter construction means which constructs (generates) a sorter having abinary tree structure or a classifier of instruction type.

An embodiment of a defect detection method using the defect inspectionapparatus (visual inspection apparatus) shown in FIG. 1 will now bedescribed.

On a semiconductor wafer 11 which is the inspection subject, a largenumber of dies 21 that should have the same pattern are arrangedregularly as shown in FIG. 2. The defect decision unit 107 in the imageprocessing unit 107 compares images in the same position of two adjacentdies, for example, compares an image in a region 22 shown in FIG. 2 withan image in a region 23 of an adjacent chip, and detects a portionhaving a difference between them as a defect.

The operation will now be described. The general control unit 15continuously moves the semiconductor wafer 11 which is the inspectionsubject into, for example, a direction opposite to a direction of a scanA shown in FIG. 2 by using the stage 12. In synchronism with thecontinuous movement of the stage 12, the image sensor 104 in thedetection unit 13 detects optical images of the inspection subject 11successively in the direction of the scan A and takes in images of thechip. The image sensor 104 in the detection unit 13 outputs the signalinput thereto to the image processing unit 14. In the image processingunit 14, first, the AD conversion unit 105 converts the input analogsignal to a digital signal, and then the pre-processing unit 106conducts shading correction and dark level correction.

The defect decision unit 107 conducts a defect decision by using amethod described later. The image cropping unit 108 crops a detectedimage, a reference image and a threshold image with a predetermined sizearound a position of a defect detected by the defect decision unit 107.The feature detection unit 109 calculates features oriented for defectsort, such as a feature representing a size of the defect, a featurerepresenting brightness (gradation value) of the defect, a featurerepresenting a shape of the defect, and a feature representinginformation of background, for each of a plurality of defect candidateson the basis of the extracted detected image and reference image. Thedefect classify unit 110 conducts classification by using a classifymethod (a classifier having a binary tree structure or a classifier ofinstruction type) constructed previously using the classifierconstruction means such as the sort condition setting unit 111, andoutputs class information for each defect. As a first classificationmethod, a classifier having a binary tree structure which finallyclassifier defects into classes by repeating the operation of dividingdefects into two as shown in FIG. 3 is used. However, it is alsopossible to provide known discriminant techniques together and make themselectable. The image information output from the image cropping unit108 (such as the detected image, reference image and threshold imagecropped with a predetermined size around the position of the defect),features of the defect output from the feature extraction unit 109(features oriented for defect class such as the feature representing thesize of the defect, the feature representing the brightness (gradationvalue) of the defect, the feature representing the shape of the defect,and the feature representing the information of the background), and thedefect class information output from the defect classify unit 110 arestored in the storage 112. They are also exhibited to the user via theuser interface unit 113.

Operation conducted in the defect classify unit 110 according to thepresent invention will now be described in detail with reference to FIG.3. FIG. 3 schematically shows a structure of the sorter having thebinary tree structure. All defect classes are included in a root node301. At a branch point 302, defects are divided into two nodes: a left(L) node 303 a and a right (R) node 304 on the basis of a predeterminedbranch condition. Such a branch operation is repeated and defects arefinally sorted into defect classes 303 a to 303 d. A branching conditionis set for each branch point. In the simplest case, one feature isselected and a threshold is set. If the feature is less than thethreshold, the defect is classified into the left group. If the featureis at least the threshold, the defect is classified into the rightgroup. It is also possible to select a plurality of features, setthresholds respectively for the features, and set conditions by usinglogical expressions. As a method to classify defects into two groups,the known two-class discriminant technique such as the support vectormachine may also be adopted.

Operation conducted in the classify condition setting unit 111 includedin the classifier construction means according to the present inventionwill now be described with reference to FIGS. 4 to 8.

FIG. 4 shows a flow of data for the classify condition setting unit 111.The image processing unit 14 in the inspection apparatus outputscoordinates, features and a detected image of each of detected defects.The review apparatus 16 conducts review and classifies on the basis ofcoordinates of defects and outputs classes and review images of thedefects. The classify condition setting unit 111 sets a classifycondition on the basis of instruction data obtained by associatingfeatures of defects output from the image processing unit 14 with defectclasses output from the review apparatus 16, and outputs theclassification condition thus set.

FIGS. 5A, 5B and 5C show an embodiment of a classification conditionsetting GUI using the user interface unit 113 in the classificationcondition setting unit 111. The binary tree structure shown in FIG. 3 isdisplayed in a tree structure display window 501. A branch pointindicated by a small square is selected by performing double click onthe branch point, and the selected branch point is displayed with adifferent color. A “register” button 502 is a button for registering atree structure already set and branch conditions at respective branchpoints as a class recipe. Associations among symbols A to D, defectclass codes input from the review apparatus 16, and defect class namesdisplayed in a tree structure are displayed in a defect class list 503.Information concerning the branching condition for the branch pointselected in the tree structure display window 501 is displayed in abranching condition display window 504. In a defect class selectionwindow 505, whether a class belongs to the left side or the right sideof the selected branch point is represented by a check mark. The checkcan be changed manually. A histogram by classes is displayed every kindof features in a feature distribution display window 506. Explanatorynotes of classes are indicated at the side of symbols AB . . . in adefect class selection window 505. A feature name of defects isdisplayed above the histogram. A check box before the feature nameindicates whether the feature is being used. The check can be changedmanually. A keyword representing a classify technique such as “rulebase” is displayed in a discriminant reference display window 507. Aconcrete discriminant reference such as, for example, “Intensity>50=>L”is displayed by depressing a “detail” button 508. Furthermore, a GUI forsetting the discriminant reference manually is displayed. With respectto the whole of classes included in L, each of the classes included inL, the whole of classes included in R, and each of the classes includedin R in order from above, the number of defects sorted into L accordingto a preset discriminant reference, the number of defects sorted into R,and a correct answer ratio (accuracy) are displayed. A purity, that is,the ratio of the number of defects in a class checked in L in the defectclass selection window 505 to the number of defects classified into Laccording to the preset discriminant reference (65%=13/20, 88%=38/43) isdisplayed in a bottom line. A correct answer ratio of the whole(81.0%=51/63) is displayed in a bottom right corner. A “setautomatically” button 510 is a button for displaying an automaticsetting mode setting window 601 shown in FIG. 6 and executing automaticsetting according to a mode which is set. A “restore” button 511 is abutton for restoring all of portions changed manually and automaticallyon the branch condition display window 504 to their original states. A“register” button 512 is a button for registering the portions changedmanually and automatically on the branch condition display window 504.

The automatic setting mode setting window 601 shown in FIG. 6 will nowbe described in a little more detail. An algorithm is provided to selecta sort technique from among various classify techniques besides the“rule base” which is a classification technique using comparison with athreshold value. An execution mode selection list 603 is provided tospecify a range for automatic setting. There are four execution modes:“1. all of the following,” “2. from defect class selection only at aselected branch point,” “3. from feature selection only at a selectedbranch point,” and “4. from discriminant reference setting only at aselected branch point.” In the ensuing description, they are referred toas modes 1 to 4 by using the numbers. The mode 1 is a mode forautomatically setting all branch conditions inclusive of a selectedbranch point and the tree structure following it. The modes 2 to 4 aremodes for automatically setting only at a selected branch point. Adifference between them will now be described with reference to FIG. 7.FIG. 7 is a diagram showing a flow for setting a branch condition in thesort condition setting unit 111. The flow includes a step of selecting adefect belonging to the left or right group (S71), a step of selecting afeature to be used for branch (S72), and a step of determining adiscriminant reference (S73). The mode 2 is a mode for setting the stepsS71 to S73 automatically. The mode 3 is a mode for executing the stepS71 manually and setting the steps S72 and S73 automatically. The mode 4is a mode for executing the steps S71 and S72 manually and setting thestep S73 automatically. A “parameter” button 604 is a button for settinga parameter corresponding to the selected algorithm. A parameter settingscreen is displayed by depressing the “parameter” button 604, andediting can be conducted. An “OK” button 605 is a button for executingthe selected algorithm and automatic setting according to the executionmode and conducting termination. A “cancel” button 606 is a button forconducting termination without executing the automatic setting.

A procedure for conducting the classify condition setting by using theGUI shown in FIGS. 5A, 5B and 5C will now be described. FIGS. 8A, 8B and8C show a display state of the GUI at the time when starting theclassify condition setting. It is supposed that defect feature data anddefect class information have been associated with each other and inputto the sort condition setting unit 111 as instruction data. The rootnode and the first branch point are displayed in the selected state inthe tree structure display window 501 (FIG. 8A). Class codes and classnames of all defect classes that can be read from the input informationare displayed in the defect class list 503 (FIG. 8B). A symbol (ABCD)corresponding to all defect classes is displayed in the root node.Although symbols corresponding to all defect classes are displayed inthe defect class selection window 505 (FIG. 8C), check is given nowhere.A histogram by classes is calculated with respect to all features anddisplayed in the feature distribution display window 506. Check is givennowhere. Nothing is displayed in the discriminant reference displaywindow 507 and an evaluation window 509.

Hereafter, a procedure for setting the classify condition manually willbe described. First, defect classes belonging to the left-side andright-side groups are selected by checking in the defect class selectionwindow 505 (step S71). It is also possible to check both L and R.Symbols of the defect classes are displayed in nodes located on the leftand right sides of the selected branch point in the tree structuredisplay window 501 according to the selection result. Subsequently, adefect feature to be used is selected by a check on the left side of thedefect feature name in the feature distribution display window 506 (stepS72). Subsequently, a discriminant reference is set by using thediscriminant reference setting GUI displayed by depressing the “detail”button 508 (step S73).

FIG. 9 is a diagram showing an embodiment of the discriminant referencesetting GUI using the user interface unit 113. Reference numeral 901denotes a threshold setting window. A histogram by classes 902 for theselected defect feature is displayed in the threshold setting window901. A defect feature name with a serial number in the range of 1 to Nadded thereto is displayed above the histogram 902. Here, N is thenumber of selected defect features.

A line 903 represents a threshold, and it is linked to a value displayedin a window 904. In the window 904, the value can be changed by taking asection of the histogram as the unit and using a spin button. In awindow 905, either “less than the threshold” or “at least the threshold”is selected. A condition concerning one feature is thus set. In thisembodiment, the condition having the serial number 1 becomes true if the“intensity” is less than 50. In a window 906, either L or R is selected.In a window 907, a logical expression representing a condition underwhich a defect is classified into the group selected in the window 906is described. Each of numerical values corresponds to the serial numberof the selected feature, and “*” represents a logical product. It isalso possible to use “+” representing a logical add and parentheses. Bydepressing an “OK” button 908, a discriminant reference is set accordingto input and the original screen is restored. By depressing a “cancel”button 909, the original screen is restored without changing anything.

The branch condition can thus be set with respect to the selected branchpoint. The correct answer ratio is calculated according to the defectclass selection and the discriminant reference setting and displayed inthe evaluation window 509. This branch condition is registered bydepressing the “register” button 512. As for the display in the treestructure display window 501 at this time, a new branch point isdisplayed under a node to which a plurality of defect classes belong,for example, as shown in FIG. 10. In this state, the “register” buttonis prevented from being depressed in order to prevent an incomplete sortcondition from being registered. Therefore, it is necessary to selectthe newly added branch point and set a branch condition according to asimilar procedure. If the second branch point or a subsequent branch hasbeen selected, defect classes that do not belong to a node located rightabove are prevented from being checked in both L and R in the defectclass selection window 505. The histogram displayed in the featuredistribution display window 506 is calculated except defects in defectclasses that do not belong to the right above node and defects that arenot sorted into the right above node according to the branch condition.

The contents displayed in the evaluation window 509 are also calculatedexcept defects in defect classes that do not belong to the right abovenode and defects that are not sorted into the right above node accordingto the branch condition, in the same way. Until addition of a branchpoint is finished, i.e., until a tree structure in which only one defectclass belongs to every terminal node is constructed, the above-describedcondition setting is repeated. The tree structure and branch conditionsat respective branch points are registered as branch conditions bydepressing the “register” button 512.

In the above-described procedure, automatic setting of a branchcondition is possible for the selected branch point or every branchpoint following the selected branch point. The “set automatically”button 510 can be depressed in any state. The execution mode 1 can beselected at any time. Processing conducted when the mode 1 has beenselected will be described later. The execution mode 2 can also beselected at any time. All the steps S71 to S73 are executedautomatically. The execution mode 3 can be selected when selection ofdefect classes belonging to L and R has already been conducted bychecking in the defect class selection window 505. The execution mode 4can be selected when selection of a feature has already been conductedby checking in the feature distribution display window 506.

FIG. 11 is a diagram for explaining a flow of processing conducted inthe branch condition setting unit 111 when the mode 1 has been selected.First, a branch condition for branching defects included in a nodelocated right above a selected branch point to left side and right sidegroups (S111). This corresponds to automatically executing the steps S71to S73. If defect classes belonging to the left side group are at leasttwo kinds (S112), a branch point is added under the left side node and abranch condition is set (S113).

This corresponds to executing the step S111 to step S116 recursively.

If a defect class of one kind belongs to the left side group at stepS112, the step S113 is skipped. If defect classes belonging to a rightside group are at least two kinds at step S114, a branch point is addedunder the right side node and a branch condition is set (S115).

Processing conducted at the step S115 is processing similar to thatconducted at the step S113.

If a defect class of one kind belongs to the left side group at the stepS114, the step S115 is skipped. The processing is thus finished (S116).Although not illustrated, pruning may be conducted by using the knownmethod after the binary tree is constructed by using the above-describedmethod.

An algorithm for automatically executing the steps S71 to S73 will nowbe described with reference to FIGS. 12A-12F. FIGS. 12A-12F show anembodiment at the time when “rule base” has been selected as analgorithm. First, the degree of separation is evaluated with respect toall combinations in dividing existing defect classes into L and R andall features. A way of dividing defect classes that yields the highestdegree of separation is selected (step S71) and a feature is selected(step S72). In FIGS. 12A and 12D, the ordinate indicates the way ofdividing defects into L and R, and the abscissa indicates the feature.FIGS. 12B, 12C, 12E and 12F show histograms of features by L and R, andthe degree of separation calculated on the basis thereof. For evaluationof the degree of separation, for example, the difference in entropybetween before and after feature observation is used. The entropy ishigh in a state in which groups to be divided are mixedly present,whereas the entropy becomes low in a state in which the groups have beenseparated. The Mahalanobis distance, the entropy gain ratio, the GINIindex, the Kullback information content, or the like may be used toevaluate the degree of separation. Subsequently, a threshold thatmaximizes the chi-square statistics with respect to the way of divisionand feature of the selected defect classes is found. It is checkedwhether L is less than the threshold or at least the threshold, and itis used as a discriminant reference (step S73). It is also possible tocalculate the correct answer rate (accuracy) for each class, and cause adefect to belong to both L and R when the correct answer ratio is lowerthan a reference value previously set as a parameter. Also when “FV(fuzzy voting)” is selected as the algorithm, processing as far as thestep S72 is conducted in the same way as that described above. In thesame way of dividing defect classes, a plurality of defect features suchas the second degree of separation and the third degree of separationmay also be selected. At the step S73, a likelifood function by classesis found for each of defect features selected on the basis of thehistogram and used as a discriminant reference. Besides, a discriminanttechnique suitable for two classes, such as the support vector machine(SVM) or the linear discriminant method, may also be applied. At thattime, all features may be used without especially conducting the featureselection. As for the evaluation of the degree of separation, a resultobtained by conducting the sorter learning and evaluating the correctanswer ratio may be used. Any method may be used as long as it ispredetermined for the algorithm which becomes the selection subject orit can be set by a parameter.

If the execution mode 2 in the automatic setting has been selected, thesteps S71 to S73 are executed only once by using a technique similar tothat described above. If the execution mode 3 has been selected,processing is executed from the step S72. However, evaluation of thedegree of separation is conducted on only a specified way of dividing,and a defect feature maximizing the degree of separation is selected.However, defect classes checked in both L and R are previously excludedfrom the calculation of the degree of separation. If the execution mode4 is selected, only the step S73 is executed automatically.

If the classify condition setting GUI according to the present inventionis used, it is possible to change the threshold after the automaticsetting has been conducted. Furthermore, it is also possible to add abranch point after the automatic setting has been conducted. It becomesmeans for approaching the goal when the purity is low. Its method willnow be described. First, the “set automatically” button 510 is selectedat the time of start of the classify condition setting shown in FIG. 9.Then, the execution mode 1 is selected in an automatic setting modesetting window 601. As a result, all tree structures and branchconditions are set automatically. Thereupon, the tree structure and theevaluation results as shown in FIGS. 5A, 5B and 5C are displayed.

Here, the number of defect classes included in L is made large, if thepurity in L is low. For example, L in the class C is also checked.Thereupon, A and C are displayed in the left side node, and a new branchpoint is added under the left side node. The added branch point isselected, and a branch condition for division into A and C is set byautomatic setting or manual setting. On the contrary, if the accuracy inL is judged to be insufficient, R in the class A is also checked. Allclasses A, B, C and D are displayed in the right side node. The branchpoint under the node and the following are set again automatically ormanually. According to the present invention, it thus becomes possibleto set the classify condition with a user's intention reflected. Forexample, it thus becomes possible to set the classify condition with auser's intention of conducting adjustment so as to make the purityand/or accuracy of important defects at least target values reflected,with a user's intention of avoiding use of a certain featureintentionally reflected, or with a user's intention of determiningfeatures to be used on the basis of knowledge reflected.

As for the defect feature data displayed in the feature distributiondisplay window 506, the output of the feature extraction unit 109 isused as it is, in the foregoing description. However, it is favorable topreviously standardize each defect feature by using the expressionx=(x−μ)/σ. Here, x is the value of a defect feature, μ is its average,and σ is its standard deviation. If a defect feature is not zero, thecorrect answer ratio is improved in some cases by raising the value ofthe defect feature to λth power, where λ is a real number which is 1 orless and which is not 0, thus converting the scale, and then conductingstandardization. Or it is favorable in some cases to use a defectfeature subjected to axial transformation using the principal componentanalysis or the like. It is also possible to use defect features with anew defect feature added by conducting some arithmetic operation such asfinding a ratio between defect features.

It is desirable to conduct such transformation and addition of defectfeatures before setting the classify condition using the classifycondition setting GUI. Or although not illustrated, it is also possibleto use a configuration in which scale transformation and axistransformation can be conducted by operation on the feature distributiondisplay window 506.

In the foregoing description, defect class information is output fromthe review apparatus 16. Alternatively, it is also possible to use aconfiguration in which the defect coordinates, the inspected image andthe review image shown in FIG. 4 with parentheses are collected in thesort condition setting unit 111 and instruction data are generated byvisualization sort.

FIGS. 13A-13D show an embodiment of a defect instruction GUI using theuser interface unit 113 for displaying the inspection image and thereview image and conducting visual classification in the sort conditionsetting unit 111. Maps representing defect positions on a wafer and adie are displayed on a wafer map display window 1301 (FIG. 13A) and adie map display window 1302 (FIG. 13B), respectively. Inspection imagesfor respective defect classes are displayed in an inspection imagedisplay window 1303 (FIG. 13C) in the order of the defect ID. Everydefect is displayed in a row of some class or a row represented as“unsorted” without duplication. By drug and drop of an image, a defectclass of the corresponding defect can be instructed. A defect image 1305and a reference image 1306 of the selected defect obtained in theinspection apparatus and a defect image 1307, a reference image 1308 anda feature list 1309 obtained in the review apparatus are displayed in aninspection information detail display window 1304 (FIG. 13D). The defectselection is conducted by clicking a defect point on the wafer map,clicking a defect point on the die map, or clicking an inspection imagein the inspection image display window 1303. According to this method,it is possible to instruct a defect class even for a defect that is notreviewed, on the basis of the inspection image. Unless a defect can bediscriminated, it should be left in the “unsorted” row. Therefore, thenumber of instruction samples can be increased. As a result, correctsort condition setting becomes possible. It is desirable to storeresults obtained by thus conducting the visualization sort in thestorage 112 as feature data with class information.

In the present embodiment, defect class codes instructed first areassigned different symbols. However, it is also conceivable to providean editing function for the defect class list 503 and make it possibleto handle a plurality of defect classes as one defect class. If thepresent function is used, improvement of the classify correct answerratio can be anticipated by handling a plurality of classes that aredifficult to separate as one class.

The visual inspection method, and apparatus, according to the presentinvention are not restricted to the visual inspection apparatus in thepresent embodiment. For example, even if the detection unit 13 is thedark field of vision type or the SEM type, the sort condition settingcan be conducted by using a similar configuration. An embodiment of aconfiguration in which a dark vision field optical system is used as thedetection unit 13 and light is applied obliquely and light scatteredfrom the subject is detected in the upper part will now be describedwith reference to FIG. 14. Light emitted from a laser light source 1401is formed in a slit form by a beam expansion optical system including aconcave lens 1402 and a convex lens 1403, a circular cone lens(including a pseudo circular cone lens having a cylindrical lensinclined about the optical axis), and a mirror 1405, and resultant lightis applied to a wafer 11 obliquely. The reason why the irradiation lightis formed in the slit form is that inspection speed is raised. Thedetection optical system for detecting the scattered light on thesurface of the wafer 11 includes a Fourier transform lens 1406, aspatial filter 1407, an inverse Fourier transform lens 1408, an NDfilter 1409, an optical filter 1410 such as a sheet polarizer, and animage sensor 104. The spatial filter 1407 is placed on a Fouriertransform plane to shield diffracted light arriving from a repeatedpattern on the wafer. On the other hand, scattered light from a defectis spread on the Fourier transform plane irregularly, and consequentlymost thereof is received by the image sensor 104 without being shielded.Therefore, the S/N is improved, and it becomes possible to detect thedefect with high sensitivity. A signal detected by the image sensor 104is input to the image processing unit 14. The defect detection, theimage cropping, the feature calculation and the defect classificationare conducted by using a method similar to the above-described method.Operation in the sort condition setting unit 111 is conducted in thesame way. The configuration in the present embodiment has one sensor 104for image detection. As an alternative configuration, it is alsopossible to provide a detection optical system for conducting detectionfrom different angles, conduct image detection by using two or moresensors, use feature data obtained by defect detection, image croppingand feature extraction together, and thereby conduct defect sort. Asanother alternative configuration, it is also possible to conductinspection twice or more times under different optical conditions,conduct coordinate matching of the detected defects, use obtainedfeature data together, and classify defects. As another alternativeconfiguration, a classify condition may be individually set by usingfeature data obtained under respective inspections. In other words, thecase where the inspection condition (including the optical condition) ischanged is also included as obtained feature data.

When executing the inspection, the configuration conducts classificationaccording to the classify condition corresponding to the inspectioncondition, adds reliability information of the defect class such as thepurity of the defect class (calculated when setting the sort condition)to the defect class, and determines a defect class for each of thedetected defects by applying a majority decision or a reliabilityweighted majority decision to defect classes added in a plurality ofinspections.

In the foregoing description, the visual inspection conducted bycomparing wafers on which patterns taking the same shape are formed hasbeen taken as an example. However, the classify condition setting methodaccording to the present invention can also be applied to inspection ofa wafer having no patterns.

An embodiment of a configuration of a defect inspection apparatus(visual inspection apparatus) for wafers having no patterns will now bedescribed with reference to FIG. 15. A semiconductor wafer 11 which isthe subject is retained by a rotational stage 1501. An illuminationoptical system 1502 includes a laser light source 1503, a beam expander1504, mirrors 1505 and 1506, and a condenser lens 1507. A detectionoptical system 1508 includes condenser lenses 1509 a and 1509 b, andphotoelectric converters 1510 a and 1510 b. A signal processing system1511 includes threshold processing units 1512 a and 1512 b, a defectdiscriminant unit 1513 and a threshold by area setting unit 1514. Aninspection method implemented in the inspection apparatus shown in FIG.15 will now be described. A laser beam emitted from the laser lightsource 1503 is expanded in beam diameter by the beam expander 1504, thennarrowed down in beam diameter by the condenser lens 1507 via themirrors 1505 and 1506, and applied onto the wafer 11. If a defect suchas a dust particle is present in a beam irradiation position on thesurface of the wafer 11, strong scattered light is generated. Thescattered light is condensed onto a light sensing plane of thephotoelectric converter 1510 a by the condenser lens 1509 a, and subjectto photoelectric conversion by the photoelectric converter 1510 a.Scattered light is detected by the condenser lens 1509 b and thephotoelectric converter 1510 b which are disposed at a different angle,in the same way. The direction in which strong scattered light isgenerated differs depending upon the size and shape of the defect.Therefore, the sensitivity can be improved by conducting detection froma plurality of angles. In addition, it is more desirable to increase thedetection system so as to cover all orientations. Outputs of thephotoelectric converters 1509 a and 1509 b are compared with a thresholdin the threshold processing units 1512 a and 1512 b. If an output isgreater than the threshold, it is detected as a defect. Detectionsignals of the threshold processing units 1512 a and 1512 b are input tothe defect discriminant unit 1513, and defect sort is conducted on thebasis of magnitudes of the signals and a ratio between the signals. Forinspecting the whole surface of the wafer 11, it is necessary to scanthe wafer 11 with the irradiation position of the laser beam. Spiralscanning is made possible by causing a straight movement of the rotationaxis so as to make the rotation axis approach the irradiation positionwhile rotating the wafer 11 by using the rotational stage 1501. Ifscanning the wafer 11 in the radius direction with the laser beam isused in combination with the straight movement although not illustrated,a straight movement corresponding to the width of the scanning with thelaser beam can be made during one revolution, and consequently the timerequired to inspect the whole surface of the wafer can be shortened.Instead of scanning with the laser beam, a slit-like beam which is longin the radius direction of the wafer 11 may be formed and applied byusing a cylindrical lens or a circular cone lens, although notillustrated. The sort condition can be set according to theabove-described method by using magnitudes and ratios of signalsdetected by a plurality of detectors as feature data.

Second Embodiment

A second embodiment of a defect classification method, and apparatus,and a defect inspection method, and apparatus, in which defects areclassified by using a classifier constructed (generated) with a classifycondition setting function according to the present invention will bedescribed in detail with reference to FIGS. 16 to 22.

The second embodiment according to the present invention differs fromthe first embodiment in that the second embodiment has a function ofspecifying user's intention (target performance of purity and accuracyfor each defect class, the whole and the worst case) with a priorityorder and a function of evaluating performance of sort according to thepreset sort condition and displaying whether the specified targetperformance is satisfied, for each item intended by the user.

The defect inspection apparatus (visual inspection apparatus) accordingto the second embodiment has a configuration similar to that of thefirst embodiment. By the way, the defect classify unit 110 according tothe present invention conducts classification by using the classifycondition preset in the classify condition setting unit 111 and outputsclass information of each defect.

As for the defect classify unit 110 in the second embodiment, the casewhere defects are classified by using a classifier having a binary treestructure on the basis of features of the defects described withreference to the first embodiment and the case where defects areclassified by using a classifier of instruction type on the basis offeatures of the defects are conceivable. When the classifier having thebinary structure is used, the classify condition setting unit 111displays whether each of items of the user's intention is satisfied withrespect to a specified branch point. Here, the user's intention is, forexample, a desire to give priority to raising accuracy or purity for aspecific defect over the correct answer ratio of the whole.

If a classifier of instruction type is used, any algorithm may be usedas long as it is a learning algorithm (classify algorithm of instructiontype). In addition, it is more desirable to prepare a plurality oflearning algorithms and make them selectable.

FIG. 16 shows an embodiment of a GUI using a user interface unit 113 forspecifying user's intention with priority orders in a classify conditionsetting unit 111. In a priority order specifying window 1601, a defectclass, selection of accuracy or purity, and target sort performance arespecified in association with a priority order. As for the defect class,it is selected from a defect class selection list 1602 and specified.Class codes and class names of all kinds included in the instructiondata, “all,” and “minimum” are included in the list. Here, “all”represents the correct answer ratio of all defects, and “minimum”represents the worst value of accuracy or purity of each class. Accuracyor purity is selected in an accuracy/purity selection list 1603. Targetperformance is set by inputting a numerical value to a target settingwindow 1604. Since setting is conducted basically in order from thehighest priority order, a new item can be input only in a row locatedright under an already set item. After the setting, the priority ordercan be changed by using priority order change buttons 1605 a and 1605 b.If “up” is pressed, the item is interchanged with an item located rightabove. If “down” is pressed, the item is interchanged with an itemlocated right under. When desiring to insert a new item, the item isinput to the bottom row and then moved to a location desired to beinserted in by using the “up” button. Depressing an “OK” button causesinput setting to be stored and the processing to be ended. Depressing a“cancel” button causes input setting to be discarded and the processingto be ended.

A classify condition setting flow in the case where a classify algorithmof instruction type (sorter of instruction type) other than the binarytree structure is used according to the second embodiment of the presentinvention will now be described with reference to FIG. 17. First, targetclassification performance by items is specified with a priority orderby using the GUI shown in FIG. 16 (S171). Subsequently, the classifycondition of the classifier of instruction type is set by learning usinga learning algorithm and a leaning specified (selected) previously atstep S172, on the basis of instruction of the defect class and defectfeature data associated therewith (S173). Then, classificationperformance according to the set classify condition is evaluated (S174).As for the evaluation method, the leave one out method is desirable. Inthe leave one out method, one sample is used as a test sample whereasremaining samples are used as instruction samples and the evaluation isrepeated by the number of defects. The evaluation result is displayedtogether with degrees of satisfaction of the user's intention specifiedby the GUI (S174).

FIG. 18 shows an embodiment of a GUI using the user interface unit 113for displaying the degrees of satisfaction of the user's intention. Asatisfaction degree evaluation display window 1702 and a confusionmatrix 1703 are displayed in an evaluation result display window 1701.The satisfaction degree evaluation display window 1702 and the confusionmatrix 1703 display the same result with different visual points. Thedefect class, selection of accuracy or purity, target performance,performance evaluation result, difference from the target, and decisionare displayed in the satisfaction degree evaluation display window 1702in the order of priority order. The confusion matrix 1703 shows anaggregate which indicates defect classes defects are classified intounder the classify condition learned about instructed correct answerdefect classes.

The accuracy, i.e., the correct answer ratio for each of defect classesunder the learned sort condition is displayed in the rightmost column.The purity, i.e., the ratio of the number of defects sorted correctly todefects sorted into a certain defect class by the learned classifycondition is displayed in the bottom row. The correct answer ratio ofthe whole is displayed in the right bottom corner. With respect to eachof items specified as to the target sort performance, the priority orderand a decision (represented by “o” or “x”) are displayed to the right ofthe accuracy or under the purity. It is possible to obtain informationas to a defect class in which an error is apt to occur by displaying theconfusion matrix. If it is judged that “OK” should be given on the basisof the evaluation result (S175), then a classify condition is set bylearning using all instruction data in response to depression of a“register” button 1704 (S176), the classify condition is registered inthe storage 112, and the processing is finished (S177). If the result isjudged to be NG, then the processing returns to the step S172, or theprocessing is finished without conducting anything by depressing an“end” button 1705.

A classify condition setting flow in the case where a classifier havinga binary tree structure is used according to a second embodiment of thepresent invention will now be described with reference to FIG. 19.First, target classification performance by items is specified with apriority order by using the GUI shown in FIG. 16 (S171). Subsequently, afirst branch point is selected on the tree structure display window 501in the GUI shown in FIG. 5 (S181). With respect to the selected branchpoint, a branch condition is set by using the GUI shown in FIG. 5(S182). Subsequently, evaluation is conducted and its result isdisplayed (S183). In other words, target performance of purity andaccuracy for each defect class, the whole and the worst case isspecified previously with a priority order (S171). After the branchcondition is set, it is evaluated for each item whether the specifiedtarget sort performance is specified and its result is displayed (S183).

FIGS. 20A and 20B show an embodiment of a GUI for displaying whethereach item is satisfied with respect to the specified branch point. FIGS.20B and 20C are different from FIGS. 5B and 5C in that targetclassification performance by items is displayed in a window 1801 and acolumn of “Opp” (abbreviation of “opposite”) which belongs to anopposite group at an upstream branch point is provided. In FIG. 20A, thefirst branch point has been selected. Therefore, a defect classbelonging to “Opp” is not present, and a defect classified to “Opp” isnot present, either. Furthermore, in the same way as FIG. 18, a priorityorder (represented by a numeral surrounded by a rectangle) and adecision (represented by “o” or “x”) are displayed to the right of theaccuracy or under the purity with respect to each of items specified asto the target sort performance.

However, items that cannot be evaluated such as the purity of the defectC or D are not displayed. Although other windows and buttons are notillustrated, they are arranged suitably.

It is determined whether “OK” should be given on the basis of theevaluation result (S184). If the result is NG, then the processingreturns to the step S182. Viewing the evaluation result, it isappreciated that the accuracy of the defect C (priority order is thesecond) does not satisfy the target. An embodiment in which the branchcondition is modified on the basis of this information is shown in FIGS.21A and 21B. First, as shown in FIG. 21A, the defect C is added as thedefect class belonging to L, and a change is made so as to divide thedefects into AC and BCD. Thereupon, evaluation results change as shownin FIG. 21A. Since the defect C belongs to both L and R, the defect Cmay be sorted into any of them and the accuracy becomes 100%. At thistime point, an item having NG (x) disappears. As shown in FIG. 21B,therefore, the next branch point is selected, a branch condition isdetermined manually or automatically, and display contents of theevaluation window 509 is updated. The L column indicates defects sortedinto the defect A and the purity can be evaluated. Since the targetperformance is not specified, however, it is not displayed. The R columnindicates defects classified into the defect C. At this time,calculation is conducted supposing that defects belonging to “Opp” canbe classified perfectly so as to facilitate the decision as to whetherthe branch condition at the selected branch point is good. In otherwords, the best case is evaluated. For example, since the defect Cbelongs to “Opp” as well, it is supposed when calculating the puritythat the defect C on the “Opp” side can be sorted with 100% in bothaccuracy and purity. Furthermore, the correct answer ratio of the wholeis also calculated supposing that defect classes (parts of B and C, andD) belonging to “Opp” are 100% in correct answer ratio.

Therefore, it is appreciated that the purity (priority order is first)of the defect C does not satisfy the target even in the best case. Inorder to satisfy the target, it becomes necessary to change thediscriminant of A and C, or add A as a defect class belonging to R andfurther increase branch points. If the evaluation result is judged to beOK (S184), then it is checked whether the branch condition is completed,i.e., the tree structure is completed (S185). If the tree structure iscompleted, then the tree structure and the branching condition at eachbranch point is registered as the branching condition and the processingis finished (S186). If the tree structure is incomplete, the next branchpoint is selected (S187) and the processing returns to the step S182.

It becomes possible to conduct modification for satisfying the targeteasily by thus evaluating and displaying whether the target is satisfiedat each branch point by using the classifier having the binary treestructure.

As heretofore described, the user conducts algorithm selection andparameter setting which are the sort condition of the classifier, andwhether the user's intention is satisfied and the difference from thetarget classification performance are displayed. As a result, thefunction of supporting the optimum algorithm selection and parametersetting can be provided.

Here, it is also conceivable to use a configuration for evaluating thedegree of satisfaction of the user's intention while conducting thelearning algorithm selection and the learning parameter settingautomatically and comprehensively and searching for an algorithm and aparameter that make the degree of satisfaction the highest. FIG. 22shows its flow. In the same way as the foregoing description, targetclassification performance by items is specified with a priority orderby using the GUI shown in FIG. 16 (S171). Learning algorithm selection(S1721) and learning parameter setting (S1722) are conducted. Learningis executed, and the degree of satisfaction of the user's intention isevaluated (S173 and S174). If all parameters are not comprehended(S1723) or if all algorithms are not comprehended (S1724), theprocessing returns to the step S1722 or S1721. The degree ofsatisfaction of the user's intention is first evaluated on the basis ofthe number of items that have satisfied the target classificationperformance without defeat from the highest priority order. In otherwords, supposing that the priority orders 1 to 5 are specified, the casewhere only the priority order 1 is satisfied is judged to be higher indegree of satisfaction than the case where the priority orders 2 to 5are satisfied. If the numbers of items that have satisfied the targetclassification performance without dissatisfaction from the highestpriority order are the same, then evaluation is conducted on the basisof the numbers of items that have satisfied the target classificationperformance. If this is also the same, evaluation is conducted by usingthe correct answer ratio of the whole. Alternatively, evaluation isconducted by using the performance of the item having the highestpriority order, or evaluation is conducted by using the worst value ofthe accuracy and purity for all defect classes. Various evaluations arethus conceivable. However, any valuation may be used as long as it ispredetermined. It is also possible to prepare some evaluationviewpoints, display results judged to be the best from respectiveviewpoints, and make the user conduct selection. The best algorithm andparameter are selected (S1725). The classify condition is set bylearning using all instruction data (S176). The classify condition isregistered in the storage 112 and the processing is finished (S177). Inother words, when to classify defects by using a classifier ofinstruction type on the basis of features of the defects, targetperformance of purity and accuracy for each defect class, the whole andthe worst case is specified previously with a priority order (S171).While comprehensively changing the learning algorithm and the learningparameter on the basis of instruction of defect feature data previouslyassociated with defect classes, it is evaluated item by item whether thespecified target classification performance is satisfied. The learningalgorithm and learning parameter that are favorable in evaluation resultare searched for (S1721 to S1725 and S174). The classify condition ofthe classifier is set by learning the learning algorithm and learningparameter obtained by the search (S173 and S176).

According to the present invention, in construction (generation) of thebinary tree structure, condition setting at each branch point of theclassifier having the binary tree structure is conducted automaticallyand manual setting is conducted for only a selected branch point and thesubsequent structure. As a result, it becomes possible to set theclassify condition with the user's intention reflected, withoutrequiring labor for conducting all setting manually. The user'sintention is, for example, to conduct adjustment so as to make thepurity and/or accuracy of important defects equal to at least a targetvalue, to intentionally avoid use of a certain feature, or to determinea feature to be used, on the basis of knowledge.

Furthermore, according to the present invention, it becomes possible inconstruction (generation) of the classifier of instruction type to setthe classify condition with the user's intention reflected.

Furthermore, according to the present invention, it becomes possible inconstruction (generation) of a classifier of the binary tree structuretype or instruction type to set the classify condition with the user'sintention reflected, by conducting specification with a priority orderas the user's intention and evaluating the degree of satisfaction of theuser's intention.

The invention may be embodied in other specific forms without departingfrom the spirit or essential characteristics thereof. The presentembodiment is therefore to be considered in all respects as illustrativeand not restrictive, the scope of the invention being indicated by theappended claims rather than by the foregoing description and all changeswhich come within the meaning and range of equivalency of the claims aretherefore intended to be embraced therein.

1. A defect classification method to classify defects by using aclassifier of instruction type on the basis of features of the defectsextracted on the basis of detected signals acquired from a defectinspection apparatus, the defect classification method comprising: apriority order specification process for previously specifying targetclassification performance of purity and accuracy for each of defectclasses, whole and in worst case, with priority order; and a classifierconstruction process for constructing the classifier of instruction typeby setting a classify condition by means of learning using a learningalgorithm and a learning parameter specified beforehand, on the basis ofinstruction of defect classes and feature data respectively associatedtherewith beforehand, wherein in the classifier construction processclassification performance under the set classify condition is evaluatedand whether the specified target classification performance is satisfiedis displayed every item.
 2. The defect classification method accordingto claim 1, wherein when to classify defects by using a classifier onthe basis of the features of the defects, defect classes are determinedby individually using features of the defect extracted on the basis ofdetected signals acquired by respective inspections under a plurality ofdifferent conditions (including an optical condition and an imageprocessing condition) in the defect inspection apparatus, individuallymaking defect sort decisions according to a plurality of defect sortconditions preset for the classifier, and unifying results of theindividual defect classify decisions by means of weighted voting ofreliability.
 3. A defect classification apparatus to classify defects byusing a classifier of instruction type on the basis of features of thedefects extracted on the basis of detected signals acquired from adefect inspection apparatus, the defect classification apparatuscomprising: priority order specification means for previously specifyingtarget classification performance of purity and accuracy for each ofdefect classes, whole and in worst case, with priority order; andclassifier construction means for constructing the classifier ofinstruction type by setting a classify condition by means of learningusing a learning algorithm and a learning parameter specifiedbeforehand, on the basis of instruction of defect classes and featuredata respectively associated therewith beforehand, wherein in theclassifier construction means classification performance under the setclassify condition is evaluated and whether the specified target sortperformance is satisfied is displayed every item.
 4. The defectclassification apparatus according to claim 3, wherein the features toclassify defects by using the classifier are features extracted on thebasis of detected signals acquired by respective inspections under aplurality of different conditions in the defect inspection apparatus.